OpenRaiser/Intern-Atlas huggingface dataset:
Assessing prompting frameworks for enhancing literature reviews among university students using ChatGPT
Venue: arXiv cs.CY 2025
Year: 2025

Writing literature reviews is a common component of university curricula, yet it often poses challenges for students. Since generative artificial intelligence (GenAI) tools have been made publicly accessible, students have been employing them for their academic writing tasks. However, there is limited evidence of structured training on how to effectively use these GenAI tools to support students in writing literature reviews. In this study, we explore how university students use one of the most popular GenAI tools, ChatGPT, to write literature reviews and how prompting frameworks can enhance their output. To this aim, prompts and literature reviews written by a group of university students were collected before and after they had been introduced to three prompting frameworks, namely CO-STAR, POSE, and Sandwich. The results indicate that after being exposed to these prompting frameworks, the students demonstrated improved prompting behaviour, resulting in more effective prompts and higher quality literature reviews. However, it was also found that the students did not fully utilise all the elements in the prompting frameworks, and aspects such as originality, critical analysis, and depth in their reviews remain areas for improvement. The study, therefore, raises important questions about the significance of utilising prompting frameworks in their entirety to maximise the quality of outcomes, as well as the extent of prior writing experience students should have before leveraging GenAI in the process of writing literature reviews. These findings are of interest for educators considering the integration of GenAI into academic writing tasks such as literature reviews or evaluating whether to permit students to use these tools.
Heads or Tails: A Simple Example of Causal Abstractive Simulation
Venue: arXiv cs.AI 2025
Year: 2025

This note illustrates how a variety of causal abstraction arXiv:1707.00819 arXiv:1812.03789, defined here as causal abstractive simulation, can be used to formalize a simple example of language model simulation. This note considers the case of simulating a fair coin toss with a language model. Examples are presented illustrating the ways language models can fail to simulate, and a success case is presented, illustrating how this formalism may be used to prove that a language model simulates some other system, given a causal description of the system. This note may be of interest to three groups. For practitioners in the growing field of language model simulation, causal abstractive simulation is a means to connect ad-hoc statistical benchmarking practices to the solid formal foundation of causality. Philosophers of AI and philosophers of mind may be interested as causal abstractive simulation gives a precise operationalization to the idea that language models are role-playing arXiv:2402.12422. Mathematicians and others working on causal abstraction may be interested to see a new application of the core ideas that yields a new variation of causal abstraction.
Efficient Large Language Models with Zero-Shot Adjustable Acceleration
Venue: arXiv cs.CL 2025
Year: 2025

Using Large Language Models (LLMs) in real-world applications presents significant challenges, particularly in balancing computational efficiency with model performance. Optimizing acceleration after fine-tuning and during inference is critical for building efficient architectures. This paper introduces Zero-Shot Adjustable Acceleration, a novel training and inference method that dynamically adjusts hardware utilization during inference without requiring additional fine-tuning. The proposed approach is applied to recent LLMs and evaluated across multiple classification and text generation tasks. Experimental results demonstrate that the method supports a wide range of zero-shot acceleration and achieves up to 11x speedup compared to the baseline.
PrediTree: A Multi-Temporal Sub-meter Dataset of Multi-Spectral Imagery Aligned With Canopy Height Maps
Venue: arXiv cs.CV 2025
Year: 2025

We present PrediTree, the first comprehensive open-source dataset designed for training and evaluating tree height prediction models at sub-meter resolution. This dataset combines very high-resolution (0.5m) LiDAR-derived canopy height maps, spatially aligned with multi-temporal and multi-spectral imagery, across diverse forest ecosystems in France, totaling 3,141,568 images. PrediTree addresses a critical gap in forest monitoring capabilities by enabling the training of deep learning methods that can predict tree growth based on multiple past observations. To make use of this PrediTree dataset, we propose an encoder-decoder framework that requires the multi-temporal multi-spectral imagery and the relative time differences in years between the canopy height map timestamp (target) and each image acquisition date for which this framework predicts the canopy height. The conducted experiments demonstrate that a U-Net architecture trained on the PrediTree dataset provides the highest masked mean squared error of $11.78\%$, outperforming the next-best architecture, ResNet-50, by around $12\%$, and cutting the error of the same experiments but on fewer bands (red, green, blue only), by around $30\%$. This dataset is publicly available on https://huggingface.co/datasets/hiyam-d/PrediTree, and both processing and training codebases are available on {GitHub}.
Column-generation for a two-dimensional multi-criteria bin-packing problem
Venue: arXiv cs.DS 2025
Year: 2025

In this study, we examine a two-dimensional bin-packing problem in printed circuit board manufacturing. Among other objectives, the number of bins, but also the number of different bin layouts, is to be minimized. As the running times of an earlier MIP presentation are only acceptable for small problem instances, we will now discuss a branch-and-price approach by using an adapted Ryan-Foster-branching. The pricing problem computes the layouts, separating the time-consuming constraints from the master problem.
FantasyHSI: Video-Generation-Centric 4D Human Synthesis In Any Scene through A Graph-based Multi-Agent Framework
Venue: arXiv cs.CV 2025
Year: 2025

Human-Scene Interaction (HSI) seeks to generate realistic human behaviors within complex environments, yet it faces significant challenges in handling long-horizon, high-level tasks and generalizing to unseen scenes. To address these limitations, we introduce FantasyHSI, a novel HSI framework centered on video generation and multi-agent systems that operates without paired data. We model the complex interaction process as a dynamic directed graph, upon which we build a collaborative multi-agent system. This system comprises a scene navigator agent for environmental perception and high-level path planning, and a planning agent that decomposes long-horizon goals into atomic actions. Critically, we introduce a critic agent that establishes a closed-loop feedback mechanism by evaluating the deviation between generated actions and the planned path. This allows for the dynamic correction of trajectory drifts caused by the stochasticity of the generative model, thereby ensuring long-term logical consistency. To enhance the physical realism of the generated motions, we leverage Direct Preference Optimization (DPO) to train the action generator, significantly reducing artifacts such as limb distortion and foot-sliding. Extensive experiments on our custom SceneBench benchmark demonstrate that FantasyHSI significantly outperforms existing methods in terms of generalization, long-horizon task completion, and physical realism. Ours project page: https://fantasy-amap.github.io/fantasy-hsi/
RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations
Venue: arXiv cs.CE 2025
Year: 2025

Physics-informed neural networks (PINNs) and neural operators, two leading scientific machine learning (SciML) paradigms, have emerged as powerful tools for solving partial differential equations (PDEs). Although increasing the training sample size generally enhances network performance, it also increases computational costs for physics-informed or data-driven training. To address this trade-off, different sampling strategies have been developed to sample more points in regions with high PDE residuals. However, existing sampling methods are computationally demanding for high-dimensional problems, such as high-dimensional PDEs or operator learning tasks. Here, we propose a residual-based adversarial-gradient moving sample (RAMS) method, which moves samples according to the adversarial gradient direction to maximize the PDE residual via gradient-based optimization. RAMS can be easily integrated into existing sampling methods. Extensive experiments, ranging from PINN applied to high-dimensional PDEs to physics-informed and data-driven operator learning problems, have been conducted to demonstrate the effectiveness of RAMS. Notably, RAMS represents the first efficient adaptive sampling approach for operator learning, marking a significant advancement in the SciML field.
Policy-driven Software Bill of Materials on GitHub: An Empirical Study
Venue: arXiv cs.SE 2025
Year: 2025

Background. The Software Bill of Materials (SBOM) is a machine-readable list of all the software dependencies included in a software. SBOM emerged as way to assist securing the software supply chain. However, despite mandates from governments to use SBOM, research on this artifact is still in its early stages. Aims. We want to understand the current state of SBOM in open-source projects, focusing specifically on policy-driven SBOMs, i.e., SBOM created to achieve security goals, such as enhancing project transparency and ensuring compliance, rather than being used as fixtures for tools or artificially generated for benchmarking or academic research purposes. Method. We performed a mining software repository study to collect and carefully select SBOM files hosted on GitHub. We analyzed the information reported in policy-driven SBOMs and the vulnerabilities associated with the declared dependencies by means of descriptive statistics. Results. We show that only 0.56% of popular GitHub repositories contain policy-driven SBOM. The declared dependencies contain 2,202 unique vulnerabilities, while 22% of them do not report licensing information. Conclusion. Our findings provide insights for SBOM usage to support security assessment and licensing.
Multi-Agent Reinforcement Learning for Task Offloading in Wireless Edge Networks
Venue: arXiv cs.LG 2025
Year: 2025

In edge computing systems, autonomous agents must make fast local decisions while competing for shared resources. Existing MARL methods often resume to centralized critics or frequent communication, which fail under limited observability and communication constraints. We propose a decentralized framework in which each agent solves a constrained Markov decision process (CMDP), coordinating implicitly through a shared constraint vector. For the specific case of offloading, e.g., constraints prevent overloading shared server resources. Coordination constraints are updated infrequently and act as a lightweight coordination mechanism. They enable agents to align with global resource usage objectives but require little direct communication. Using safe reinforcement learning, agents learn policies that meet both local and global goals. We establish theoretical guarantees under mild assumptions and validate our approach experimentally, showing improved performance over centralized and independent baselines, especially in large-scale settings.
Toward a Holistic Multi-Criteria Trajectory Evaluation Framework for Autonomous Driving in Mixed Traffic Environment
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents a unified framework for the evaluation and optimization of autonomous vehicle trajectories, integrating formal safety, comfort, and efficiency criteria. An innovative geometric indicator, based on the analysis of safety zones using adaptive ellipses, is used to accurately quantify collision risks. Our method applies the Shoelace formula to compute the intersection area in the case of misaligned and time-varying configurations. Comfort is modeled using indicators centered on longitudinal and lateral jerk, while efficiency is assessed by overall travel time. These criteria are aggregated into a comprehensive objective function solved using a PSO based algorithm. The approach was successfully validated under real traffic conditions via experiments conducted in an urban intersection involving an autonomous vehicle interacting with a human-operated vehicle, and in simulation using data recorded from human driving in real traffic.
Metamorphic Testing of Multimodal Human Trajectory Prediction
Venue: arXiv cs.SE 2025
Year: 2025

Context: Predicting human trajectories is crucial for the safety and reliability of autonomous systems, such as automated vehicles and mobile robots. However, rigorously testing the underlying multimodal Human Trajectory Prediction (HTP) models, which typically use multiple input sources (e.g., trajectory history and environment maps) and produce stochastic outputs (multiple possible future paths), presents significant challenges. The primary difficulty lies in the absence of a definitive test oracle, as numerous future trajectories might be plausible for any given scenario. Objectives: This research presents the application of Metamorphic Testing (MT) as a systematic methodology for testing multimodal HTP systems. We address the oracle problem through metamorphic relations (MRs) adapted for the complexities and stochastic nature of HTP. Methods: We present five MRs, targeting transformations of both historical trajectory data and semantic segmentation maps used as an environmental context. These MRs encompass: 1) label-preserving geometric transformations (mirroring, rotation, rescaling) applied to both trajectory and map inputs, where outputs are expected to transform correspondingly. 2) Map-altering transformations (changing semantic class labels, introducing obstacles) with predictable changes in trajectory distributions. We propose probabilistic violation criteria based on distance metrics between probability distributions, such as the Wasserstein or Hellinger distance. Conclusion: This study introduces tool, a MT framework for the oracle-less testing of multimodal, stochastic HTP systems. It allows for assessment of model robustness against input transformations and contextual changes without reliance on ground-truth trajectories.
Animer une base de connaissance: des ontologies aux mod{è}les d'I.A. g{é}n{é}rative
Venue: arXiv cs.DL 2025
Year: 2025

In a context where the social sciences and humanities are experimenting with non-anthropocentric analytical frames, this article proposes a semiotic (structural) reading of the hybridization between symbolic AI and neural (or sub-symbolic) AI based on a field of application: the design and use of a knowledge base for area studies. We describe the LaCAS ecosystem -- Open Archives in Linguistic and Cultural Studies (thesaurus; RDF/OWL ontology; LOD services; harvesting; expertise; publication), deployed at Inalco (National Institute for Oriental Languages and Civilizations) in Paris with the Okapi (Open Knowledge and Annotation Interface) software environment from Ina (National Audiovisual Institute), which now has around 160,000 documentary resources and ten knowledge macro-domains grouping together several thousand knowledge objects. We illustrate this approach using the knowledge domain ''Languages of the world'' (~540 languages) and the knowledge object ''Quechua (language)''. On this basis, we discuss the controlled integration of neural tools, more specifically generative tools, into the life cycle of a knowledge base: assistance with data localization/qualification, index extraction and aggregation, property suggestion and testing, dynamic file generation, and engineering of contextualized prompts (generic, contextual, explanatory, adjustment, procedural) aligned with a domain ontology. We outline an ecosystem of specialized agents capable of animating the database while respecting its symbolic constraints, by articulating model-driven and data-driven methods.
Towards Trustworthy Vital Sign Forecasting: Leveraging Uncertainty for Prediction Intervals
Venue: arXiv cs.LG 2025
Year: 2025

Vital signs, such as heart rate and blood pressure, are critical indicators of patient health and are widely used in clinical monitoring and decision-making. While deep learning models have shown promise in forecasting these signals, their deployment in healthcare remains limited in part because clinicians must be able to trust and interpret model outputs. Without reliable uncertainty quantification -- particularly calibrated prediction intervals (PIs) -- it is unclear whether a forecasted abnormality constitutes a meaningful warning or merely reflects model noise, hindering clinical decision-making. To address this, we present two methods for deriving PIs from the Reconstruction Uncertainty Estimate (RUE), an uncertainty measure well-suited to vital-sign forecasting due to its sensitivity to data shifts and support for label-free calibration. Our parametric approach assumes that prediction errors and uncertainty estimates follow a Gaussian copula distribution, enabling closed-form PI computation. Our non-parametric approach, based on k-nearest neighbours (KNN), empirically estimates the conditional error distribution using similar validation instances. We evaluate these methods on two large public datasets with minute- and hour-level sampling, representing high- and low-frequency health signals. Experiments demonstrate that the Gaussian copula method consistently outperforms conformal prediction baselines on low-frequency data, while the KNN approach performs best on high-frequency data. These results underscore the clinical promise of RUE-derived PIs for delivering interpretable, uncertainty-aware vital sign forecasts.
LongCat-Flash Technical Report
Venue: arXiv cs.CL 2025
Year: 2025

We introduce LongCat-Flash, a 560-billion-parameter Mixture-of-Experts (MoE) language model designed for both computational efficiency and advanced agentic capabilities. Stemming from the need for scalable efficiency, LongCat-Flash adopts two novel designs: (a) Zero-computation Experts, which enables dynamic computational budget allocation and activates 18.6B-31.3B (27B on average) per token depending on contextual demands, optimizing resource usage. (b) Shortcut-connected MoE, which enlarges the computation-communication overlap window, demonstrating notable gains in inference efficiency and throughput compared to models of a comparable scale. We develop a comprehensive scaling framework for large models that combines hyperparameter transfer, model-growth initialization, a multi-pronged stability suite, and deterministic computation to achieve stable and reproducible training. Notably, leveraging the synergy among scalable architectural design and infrastructure efforts, we complete model training on more than 20 trillion tokens within 30 days, while achieving over 100 tokens per second (TPS) for inference at a cost of \$0.70 per million output tokens. To cultivate LongCat-Flash towards agentic intelligence, we conduct a large-scale pre-training on optimized mixtures, followed by targeted mid- and post-training on reasoning, code, and instructions, with further augmentation from synthetic data and tool use tasks. Comprehensive evaluations demonstrate that, as a non-thinking foundation model, LongCat-Flash delivers highly competitive performance among other leading models, with exceptional strengths in agentic tasks. The model checkpoint of LongCat-Flash is open-sourced to foster community research. LongCat Chat: https://longcat.ai Hugging Face: https://huggingface.co/meituan-longcat GitHub: https://github.com/meituan-longcat
The AudioMOS Challenge 2025
Venue: arXiv cs.SD 2025
Year: 2025

This is the summary paper for the AudioMOS Challenge 2025, the very first challenge for automatic subjective quality prediction for synthetic audio. The challenge consists of three tracks. The first track aims to assess text-to-music samples in terms of overall quality and textual alignment. The second track is based on the four evaluation dimensions of Meta Audiobox Aesthetics, and the test set consists of text-to-speech, text-to-audio, and text-to-music samples. The third track focuses on synthetic speech quality assessment in different sampling rates. The challenge attracted 24 unique teams from both academia and industry, and improvements over the baselines were confirmed. The outcome of this challenge is expected to facilitate development and progress in the field of automatic evaluation for audio generation systems.
LinkBo: An Adaptive Single-Wire, Low-Latency, and Fault-Tolerant Communications Interface for Variable-Distance Chip-to-Chip Systems
Venue: arXiv cs.AR 2025
Year: 2025

Cost-effective embedded systems necessitate utilizing the single-wire communication protocol for inter-chip communication, thanks to its reduced pin count in comparison to the multi-wire I2C or SPI protocols. However, current single-wire protocols suffer from increased latency, restricted throughput, and lack of robustness. This paper presents LinkBo, an innovative single-wire protocol that offers reduced latency, enhanced throughput, and greater robustness with hardware-interrupt for variable-distance inter-chip communication. The LinkBo protocol-level guarantees that high-priority messages are delivered with an error detection feature in just 50.4 $μ$s, surpassing current commercial options, 1-wire and UNI/O by at least 20X and 6.3X, respectively. In addition, we present the hardware architecture for this new protocol and its performance evaluation on a hardware platform consisting of two FPGAs. Our findings demonstrate that the protocol reliably supports wire lengths up to 15 meters with a data rate of 300 kbps, while reaching a maximum data rate of 7.5 Mbps over an 11 cm wire, providing reliable performance for varying inter-chip communication distances.
Advanced Torrential Loss Function for Precipitation Forecasting
Venue: arXiv cs.LG 2025
Year: 2025

Accurate precipitation forecasting is becoming increasingly important in the context of climate change. In response, machine learning-based approaches have recently gained attention as an emerging alternative to traditional methods such as numerical weather prediction and climate models. Nonetheless, many recent approaches still rely on off-the-shelf loss functions, and even the more advanced ones merely involve optimization processes based on the critical success index (CSI). The problem, however, is that CSI may become ineffective during extended dry periods when precipitation remains below the threshold, rendering it less than ideal as a criterion for optimization. To address this limitation, we introduce a simple penalty expression and reinterpret it as a quadratic unconstrained binary optimization (QUBO) formulation. Ultimately, the resulting QUBO formulation is relaxed into a differentiable advanced torrential (AT) loss function through an approximation process. The proposed AT loss demonstrates its superiority through the Lipschitz constant, forecast performance evaluations, consistency experiments, and ablation studies with the operational model.